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What can we do?
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Offline Reinforcement Learning and Model-Based Optimization
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- 1 Intro
- 2 What makes modern machine learning word
- 3 Predictive models are very powerful!
- 4 Automated decision making is very powerf
- 5 First setting: data-driven reinforcement lear
- 6 Second setting: data-driven model-based optimization
- 7 Off-policy RL: a quick primer
- 8 What's the problem?
- 9 Distribution shift in a nutshell
- 10 How do prior methods address this?
- 11 Learning with Q-function lower bounds Algorithm
- 12 Does the bound hold in practice?
- 13 How does CQL compare?
- 14 Predictive modeling and design
- 15 What's wrong with just doing prediction?
- 16 The model-based optimization problem
- 17 Uncertainty and extrapolation
- 18 What can we do?
- 19 Model inversion networks (MINS)
- 20 Putting it all together
- 21 Experimental results
- 22 Some takeaways
- 23 Some concluding remarks
